On the Use of an In-Package Dielectric Lens Antenna for Radar-Based Applications
Bibliographic record
Abstract
Extensive research on millimeter-wave (mm-wave) chipset solutions has led to a reduction in size and cost while adding sensitivity and accuracy. Recent mm-wave chipset solutions using antenna-in package approaches were developed for various applications with wide beamwidth and low gain. However, for some applications, such as gait monitoring in a long corridor/hallway, there is a strong need to achieve a higher gain with narrower beamwidth. This will increase the signal-to-noise ratio (SNR) and the transmit/receive detection range of the system while mitigating reflections from the surrounding environment as well as reducing the multipath effects. An add-on dielectric lens is an appealing solution as it improves the system performance while using the existing on-chip or on-printed circuit board (PCB) antenna solutions. Using a low-cost and rapid manufacturing 3-D printing technology, an in-package 3-D printed hyperbola-based dielectric lens antenna for an mm-wave radar is designed, fabricated, and tested. This design leads to a low-cost, lightweight, easy-to-fabricate, and easy-to-integrate mm-wave radar system. The comprehensive parametric analyses of the effect of the lens dimensions, dielectric permittivity, and focal length on the radiation pattern are intensively investigated. All measurement results align with the theoretical analysis and simulation results. Compared with the radar system without the lens, the in-package 3-D printed dielectric lens antenna provides more than 14-dB gain improvement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".